The present invention relates to improving the detection of acute myocardial infarction in the presence of certain ECG confounders, and more specifically to a method for improving such detection effectively by modeling and then removing the effect of a selected confounder on the ST segment of the PQRST ECG waveform.
Detection of acute myocardial infarction (AMI) in the presence of certain ECG confounders is challenging both for commercial electrocardiograph (ECG) algorithms, and for clinicians. The combined prevalence of Left Bundle Branch Block (LBBB), right Bundle Branch Block (RBBB), Left Ventricular Hypertrophy (LVH), and Left Ventricular Hypertrophy with STT (from the ST-T portion of the ECG waveform) Abnormality (LVH/STT) in populations of patients with documented AMI can be significant, for example, as large as about 25%. The presence of such a confounder presents a significant hurdle to the correct and accurate detection of AMI evidence in an ECG waveform, and typically does this in a variety of ways, including both the masking and mimicking of AMI's ECG “signature”, principally in the ST segment of a traditional PQRST ECG waveform. This prevalence, and the obscuring effects of these confounders, highlight the need to aid clinicians in differentially diagnosing these confounding conditions from AMI.
The present invention addresses this need in a simple, practical and effective manner. Proposed according to the invention is a unique modeling and normalization procedure which focuses attention on the characteristics of the ST segment of the PQRST waveform. In particular, practice of the invention involves modeling the respective effects of the above-mentioned, several ECG confounders on this segment of the ECG waveform, thus to create, effectively, an associated reference ECG waveform that relates to each of the named, culprit confounders.
Simply and broadly stated, the method of the invention includes the steps of (a) creating a reference ECG waveform model which possesses the characteristic of an ECG waveform that is influenced by the presence of a particular selected confounder, (b) using that model, linking it relationally with an appropriate ECG purge algorithm which, in cooperation with the model, can be applied to a subject's collected ECG waveform to remove the influence of the confounder, and (c) applying that linked model and purge algorithm to such a collected ECG waveform, thus to produce a purge-processed ECG waveform that lacks the influence of the selected confounder.
These and other features involved in the implementation and practice of the present invention will now become more fully apparent as the detailed description which shortly follows is read in conjunction with the accompanying drawings.